The design and manufacture of new biodegradable and bioderived polymeric materials has traditionally taken place through experimentation and material characterisation. However, cutting-edge computational methods now provide a less expensive and more efficient approach to innovative biopolymer design and scale-up. In particular, the holistic framework provided by Materials 4.0 combines multiscale simulations and computational modelling with theory and next-generation informatics (big data integration and artificial intelligence) to model biopolymer structures, understand their flow and processibility, and predict their properties. These computational methods are being utilised to model and forecast the properties of a wide variety of biopolymeric materials, including the large family of biodegradable polyesters along with lignocellulosics, polysaccharides, proteinaceous materials, natural rubber, and so on. Ranging from quantum- to macroscale, computational modelling acts as a complement to traditional experimental techniques, probing molecular structure and intramolecular interactions as well as reaction mechanisms. This enables further kinetic modelling studies and molecular simulations. The research has been further expanded to include the use of machine learning approaches for material property optimisation in conjunction with expert knowledge and relevant experimental data. Aside from the modelling of structure-property relationships, computational modelling has also been used to predict the effect of biopolymer modifications and the influence of external factors such as the application of external fields or applied stress and the effects of moisture. In summary, there is a fast-developing library of computational modelling data for biopolymers, and the development of Materials 4.0 in this sector has enabled greater flexibility in design and processing options in advance of more expensive and time-consuming testing.

中文翻译：

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回顾用于生物降解和生物基聚合物建模、设计和制造的计算方法


传统上，新型可生物降解和生物衍生聚合物材料的设计和制造是通过实验和材料表征进行的。然而，尖端的计算方法现在为创新的生物聚合物设计和放大提供了一种更便宜、更高效的方法。特别是，材料 4.0 提供的整体框架将多尺度模拟和计算建模与理论和下一代信息学（大数据集成和人工智能）相结合，以模拟生物聚合物结构，了解其流动和可加工性，并预测其特性。这些计算方法被用于建模和预测各种生物聚合物材料的特性，包括一大类可生物降解的聚酯以及木质纤维素、多糖、蛋白质材料、天然橡胶等。从量子到宏观尺度，计算建模是传统实验技术的补充，探索分子结构和分子内相互作用以及反应机制。这使得进一步的动力学建模研究和分子模拟成为可能。研究进一步扩展，包括使用机器学习方法结合专业知识和相关实验数据进行材料性能优化。除了结构-性能关系的建模外，计算建模还被用于预测生物聚合物改性的影响和外部因素的影响，例如外部磁场的应用或施加的应力和水分的影响。 总之，生物聚合物有一个快速发展的计算建模数据库，该领域材料 4.0 的开发在更昂贵和耗时的测试之前为设计和加工选项提供了更大的灵活性。